chore: import upstream snapshot with attribution

code/ch03/README.md

@@ -0,0 +1,27 @@
+Sebastian Raschka, 2015
+
+Python Machine Learning - Code Examples
+
+
+## Chapter 3 - A Tour of Machine Learning Classifiers Using Scikit-learn
+
+- Choosing a classification algorithm
+- First steps with scikit-learn
+    - Training a perceptron via scikit-learn
+- Modeling class probabilities via logistic regression
+    - Logistic regression intuition and conditional probabilities
+    - Learning the weights of the logistic cost function
+    - Training a logistic regression model with scikit-learn
+    - Tackling overfitting via regularization
+- Maximum margin classification with support vector machines
+    - Maximum margin intuition
+    - Dealing with the nonlinearly separable case using slack variables
+    - Alternative implementations in scikit-learn
+- Solving nonlinear problems using a kernel SVM
+    - Using the kernel trick to find separating hyperplanes in higher dimensional space
+- Decision tree learning
+    - Maximizing information gain – getting the most bang for the buck
+    - Building a decision tree
+    - Combining weak to strong learners via random forests
+- K-nearest neighbors – a lazy learning algorithm
+- Summary